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A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study

Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific...

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Main Authors:	Jafari, Habib, Shohaimi, Shamarina, Salari, Nader, Kiaei, Ali Akbar, Najafi, Farid, Khazaei, Soleiman, Niaparast, Mehrdad, Abdollahi, Anita, Mohammadi, Masoud
Format:	Article
Published:	Public Library of Science 2022
Online Access:	http://psasir.upm.edu.my/id/eprint/100149/ https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262701
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spelling	my.upm.eprints.1001492024-07-17T03:34:39Z http://psasir.upm.edu.my/id/eprint/100149/ A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study Jafari, Habib Shohaimi, Shamarina Salari, Nader Kiaei, Ali Akbar Najafi, Farid Khazaei, Soleiman Niaparast, Mehrdad Abdollahi, Anita Mohammadi, Masoud Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability. Public Library of Science 2022-01-20 Article PeerReviewed Jafari, Habib and Shohaimi, Shamarina and Salari, Nader and Kiaei, Ali Akbar and Najafi, Farid and Khazaei, Soleiman and Niaparast, Mehrdad and Abdollahi, Anita and Mohammadi, Masoud (2022) A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study. PLoS ONE, 17 (1). art. no. 262701. pp. 1-20. ISSN 1932-6203 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262701 10.1371/journal.pone.0262701
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
description	Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.
format	Article
author	Jafari, Habib Shohaimi, Shamarina Salari, Nader Kiaei, Ali Akbar Najafi, Farid Khazaei, Soleiman Niaparast, Mehrdad Abdollahi, Anita Mohammadi, Masoud
spellingShingle	Jafari, Habib Shohaimi, Shamarina Salari, Nader Kiaei, Ali Akbar Najafi, Farid Khazaei, Soleiman Niaparast, Mehrdad Abdollahi, Anita Mohammadi, Masoud A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study
author_facet	Jafari, Habib Shohaimi, Shamarina Salari, Nader Kiaei, Ali Akbar Najafi, Farid Khazaei, Soleiman Niaparast, Mehrdad Abdollahi, Anita Mohammadi, Masoud
author_sort	Jafari, Habib
title	A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study
title_short	A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study
title_full	A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study
title_fullStr	A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study
title_full_unstemmed	A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study
title_sort	full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and shapley values: the ravansar county anthropometric cohort study
publisher	Public Library of Science
publishDate	2022
url	http://psasir.upm.edu.my/id/eprint/100149/ https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0262701
_version_	1805889539940548608
score	13.211869

A full pipeline of diagnosis and prognosis the risk of chronic diseases using deep learning and Shapley values: the Ravansar county anthropometric cohort study

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